Resources

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Topics

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• Lecture 20 - Π GRUs [Notebook]
• Lecture 20 - Π GRUs [Notebook]
• Lecture 20 - Π GRUs [Notebook]
• Lecture 19 - ς RNNs [Notebook]
• Lecture 18 - 𝗈 State of the art models (SOTA) and Transfer Learning [Notebook]
• Lecture 18 - 𝗈 State of the art models (SOTA) and Transfer Learning [Notebook]
• Lecture 13 - Hierarchical Models (Lab) [Notebook]
• Feed-Forward Neural Networks vs Convolution Neural Networks [Notebook]
• Lecture 12 - Bayesian statistics (part 4) [Notebook]
• Lecture 12 - Bayesian statistics (part 4) [Notebook]
• Lecture 10 - Bayes. PyMC3 [Notebook]
• Lecture 8 - Clustering in Python (Lab) [Notebook]
• Lecture 8 - Clustering in Python (Lab) [Notebook]
• Lecture 7 - Bayesian statistics (part 1) [Notebook]
• Lecture 7 - Bayesian statistics (part 1) [Notebook]
• Lecture 6 - Unsupervised learning, cluster analysis (part 2) [Notebook]
• Lecture 5.5 - pyGAM [Notebook]
• Lecture 5.5 - pyGAM with some additions [Notebook]
• Lecture 5 - Unsupervised learning, cluster analysis (part 1) [Notebook]
• Lecture 4 - Splines, Smoothers, and GAMs (part 3) [Notebook]
• Lecture 2 - Splines, Smoothers, and GAMs (part 1) [Notebook]
• Lecture 1 - Splines, Smoothers, and GAMs (part 1) [Notebook]
• Sections 02:
• ?? [Notebook]
• Sections 01:
• Sections 03:

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• Advanced Sections 05:
• Advanced Sections 06:
• Sections 06:
• Sections 07:

AlexNet

• Lecture 18: 𝗈 State of the art models (SOTA) and Transfer Learning

attention

• Lecture 28: ο Variational AE 2
• Lecture 28: ο Variational AE 2 [Notebook]
• Lecture 28: ο Variational AE 2 [Notebook]
• Lecture 27: τ AE the Bayesian Approach
• Lecture 27: τ AE the Bayesian Approach [Notebook]
• Lecture 27: τ AE the Bayesian Approach [Notebook]
• Lecture 26: ⍵ AutoEncoder (AE)
• Lecture 26: ⍵ AutoEncoder (AE) [Notebook]
• Lecture 26: ⍵ AutoEncoder (AE) [Notebook]
• Lecture 26: ⍵ AutoEncoder (AE) [Notebook]
• Lecture 25: 🤖 (Transformers II) NLP 4/4
• Lecture 25: 🤖 (Transformers II) NLP 4/4 [Notebook]
• Lecture 24: 🧠 Attention (Transformers I) NLP 3/4
• Lecture 24: 🧠 Attention (Transformers I) NLP 3/4 [Notebook]

Bayes Rule

• Lecture 9: Bayesian statistics (part 2)

Bayesian Neural Networks

• Advanced Section 4: Variational Inference

Bayesian Statistics

• Lecture 7: Bayesian statistics (part 1)

Bellman Equation

• Lecture 32: ά Bellman equation, Optimality and Recursive algorithms
• Lecture 32: ά Bellman equation, Optimality and Recursive algorithms [Notebook]

Bernoulli

• Lecture 9: Bayesian statistics (part 2)

Bet Distribution

• Lecture 10: Bayes PyMC3

Bidirectional

• Lecture 19: ς RNNs

class repo

• Lecture 3: Setup and Review of statsmodels
• Lecture 3: Setup and Review of statsmodels [Notebook]
• Lecture 3: Setup and Review of statsmodels [Notebook]

Cluster Analysis

• Lecture 8: Clustering in Python (Lab)
• Lecture 6: Unsupervised learning cluster analysis (part 2)
• Lecture 5: Unsupervised learning cluster analysis (part 1)

CNNs

• Lecture 17: λ Saliency maps
• Investigating CNNs [Notebook]
• Lecture 16: ύ Backprop max pooling, Receptive Fields and feature map viz
• Image Occlusion [Notebook]
• Investigating CNNs [Notebook]
• Lecture 15: ⍺ CNNs Pooling and CNNs Structure
• Avg vs Max Pooling [Notebook]
• Pooling Mechanics [Notebook]
• Lecture 14: Π CNNs basics

ConceptNet

• Lecture 23: 🔢 Language Representations NLP 2/4
• Lecture 23: 🔢 Language Representations NLP 2/4 [Notebook]

conda

• Lecture 3: Setup and Review of statsmodels
• Lecture 3: Setup and Review of statsmodels [Notebook]
• Lecture 3: Setup and Review of statsmodels [Notebook]

Confidence Intervals

• Lecture 7: Bayesian statistics (part 1)

Content

• Lecture 1: Splines Smoothers and GAMs (part 1)

Convolutional Neural Network

• Lecture 17: λ Saliency maps
• Investigating CNNs [Notebook]
• Lecture 16: ύ Backprop max pooling, Receptive Fields and feature map viz
• Image Occlusion [Notebook]
• Investigating CNNs [Notebook]
• Lecture 15: ⍺ CNNs Pooling and CNNs Structure
• Avg vs Max Pooling [Notebook]
• Pooling Mechanics [Notebook]
• Lecture 14: Π CNNs basics

csaps

• Lecture 5_5: Smoothers pyGAM csaps

Cubic Polynomia

• Lecture 4: Splines Smoothers and GAMs (part 3)
• Lecture 2: Splines Smoothers and GAMs (part 2)

DCGAN

• Lecture 29: π GANs
• Lecture 29: π GANs [Notebook]

DenseNet

• Lecture 18: 𝗈 State of the art models (SOTA) and Transfer Learning

Discourse

• Lecture 22: 💬 Language Modelling NLP 1/4
• Lecture 22: 💬 Language Modelling NLP 1/4 [Notebook]

echo-state

• Advanced Section 2: Echo-State Reservoir Computing

Evaluating GANs

• Lecture 30: ⍺ GANs DOS
• Lecture 30: ⍺ GANs DOS [Notebook]

Exploration vs Exploitation

• Lecture 31: π Reinforcement Learning: Basics
• Lecture 31: π Reinforcement Learning: Basics [Notebook]

FAS onDemand

• Lecture 3: Setup and Review of statsmodels
• Lecture 3: Setup and Review of statsmodels [Notebook]
• Lecture 3: Setup and Review of statsmodels [Notebook]

GANs

• Lecture 30: ⍺ GANs DOS
• Lecture 30: ⍺ GANs DOS [Notebook]
• Lecture 29: π GANs
• Lecture 29: π GANs [Notebook]

Gates

• Lecture 20: Π GRUs

generative model

• Lecture 29: π GANs
• Lecture 29: π GANs [Notebook]

Generative Models

• Lecture 10: Bayes PyMC3

GoogLeNet

• Lecture 18: 𝗈 State of the art models (SOTA) and Transfer Learning

Graphs

• Lecture 8: Clustering in Python (Lab)
• Lecture 6: Unsupervised learning cluster analysis (part 2)
• Lecture 5: Unsupervised learning cluster analysis (part 1)

GRU

• Lecture 21: ⍴ LSTMs
• Lecture 21 - ⍴ LSTMs [Notebook]
• Lecture 20: Π GRUs

Help

• Lecture 1: Splines Smoothers and GAMs (part 1)

Hierarchical Clustering

• Lecture 8: Clustering in Python (Lab)
• Lecture 6: Unsupervised learning cluster analysis (part 2)
• Lecture 5: Unsupervised learning cluster analysis (part 1)

Instructor

• Lecture 1: Splines Smoothers and GAMs (part 1)

Inter-Observation Distances

• Lecture 8: Clustering in Python (Lab)
• Lecture 6: Unsupervised learning cluster analysis (part 2)
• Lecture 5: Unsupervised learning cluster analysis (part 1)

Intro

• Lecture 1: Splines Smoothers and GAMs (part 1)

KL-Divergence

• Advanced Section 4: Variational Inference

Language Modeling

• Lecture 22: 💬 Language Modelling NLP 1/4
• Lecture 22: 💬 Language Modelling NLP 1/4 [Notebook]

Least Squares Regression

• Lecture 4: Splines Smoothers and GAMs (part 3)
• Lecture 2: Splines Smoothers and GAMs (part 2)

LeNet

• Lecture 18: 𝗈 State of the art models (SOTA) and Transfer Learning

Likelihood

• Lecture 9: Bayesian statistics (part 2)

Linear Algebra

• Lecture 8: Clustering in Python (Lab)

LSTM

• Lecture 21: ⍴ LSTMs
• Lecture 21 - ⍴ LSTMs [Notebook]

MCMC

• Lecture 12: Bayesian statistics (part 4)

Metropolis

• Lecture 12: Bayesian statistics (part 4)

MobileNet

• Lecture 18: 𝗈 State of the art models (SOTA) and Transfer Learning

Modal collapse

• Lecture 30: ⍺ GANs DOS
• Lecture 30: ⍺ GANs DOS [Notebook]

Monte Carlo

• Lecture 11: Bayesian statistics (part 3)

Morphology

• Lecture 22: 💬 Language Modelling NLP 1/4
• Lecture 22: 💬 Language Modelling NLP 1/4 [Notebook]

MPL

• Lecture 17: λ Saliency maps
• Investigating CNNs [Notebook]
• Lecture 16: ύ Backprop max pooling, Receptive Fields and feature map viz
• Image Occlusion [Notebook]
• Investigating CNNs [Notebook]
• Lecture 15: ⍺ CNNs Pooling and CNNs Structure
• Avg vs Max Pooling [Notebook]
• Pooling Mechanics [Notebook]
• Lecture 14: Π CNNs basics

Neural Networks

• Lecture 17: λ Saliency maps
• Investigating CNNs [Notebook]
• Lecture 16: ύ Backprop max pooling, Receptive Fields and feature map viz
• Image Occlusion [Notebook]
• Investigating CNNs [Notebook]
• Lecture 15: ⍺ CNNs Pooling and CNNs Structure
• Avg vs Max Pooling [Notebook]
• Pooling Mechanics [Notebook]
• Lecture 14: Π CNNs basics

Numpy

• Lecture 8: Clustering in Python (Lab)

object detection

• Advanced Section 1: Semantic Segmentation and Object Detection

Off-policy

• Lecture 33: ς Deep Q-Learning
• Lecture 33: ς Deep Q-Learning [Notebook]

On-policy

• Lecture 33: ς Deep Q-Learning
• Lecture 33: ς Deep Q-Learning [Notebook]

Ordering

• Lecture 19: ς RNNs

p-Value

• Lecture 7: Bayesian statistics (part 1)

Policy Evaluation

• Lecture 32: ά Bellman equation, Optimality and Recursive algorithms
• Lecture 32: ά Bellman equation, Optimality and Recursive algorithms [Notebook]

Policy Improvement

• Lecture 32: ά Bellman equation, Optimality and Recursive algorithms
• Lecture 32: ά Bellman equation, Optimality and Recursive algorithms [Notebook]

Policy Iteration vs Value Iteration

• Lecture 33: ς Deep Q-Learning
• Lecture 33: ς Deep Q-Learning [Notebook]

Polynomial Regression

• Lecture 4: Splines Smoothers and GAMs (part 3)
• Lecture 2: Splines Smoothers and GAMs (part 2)

Posterior

• Lecture 9: Bayesian statistics (part 2)

Prior

• Lecture 9: Bayesian statistics (part 2)

Probability

• Lecture 7: Bayesian statistics (part 1)

pyGAM

• Lecture 5_5: Smoothers pyGAM csaps

PyMC3

• Lecture 10: Bayes PyMC3

Q - Learning

• Lecture 33: ς Deep Q-Learning
• Lecture 33: ς Deep Q-Learning [Notebook]

Reinforcement Learning

• Lecture 33: ς Deep Q-Learning
• Lecture 33: ς Deep Q-Learning [Notebook]
• Lecture 32: ά Bellman equation, Optimality and Recursive algorithms
• Lecture 32: ά Bellman equation, Optimality and Recursive algorithms [Notebook]
• Lecture 31: π Reinforcement Learning: Basics
• Lecture 31: π Reinforcement Learning: Basics [Notebook]

Rejection Sampling

• Lecture 11: Bayesian statistics (part 3)

ReLU

• Lecture 4: Splines Smoothers and GAMs (part 3)
• Lecture 2: Splines Smoothers and GAMs (part 2)

Reparameterization Trick

• Advanced Section 4: Variational Inference

reservoir computing

• Advanced Section 2: Echo-State Reservoir Computing

ResNet

• Lecture 18: 𝗈 State of the art models (SOTA) and Transfer Learning

RNN

• Lecture 19: ς RNNs

RNN Issues

• Lecture 20: Π GRUs

SARSA

• Lecture 33: ς Deep Q-Learning
• Lecture 33: ς Deep Q-Learning [Notebook]

semantic segmentation

• Advanced Section 1: Semantic Segmentation and Object Detection

Semantics

• Lecture 22: 💬 Language Modelling NLP 1/4
• Lecture 22: 💬 Language Modelling NLP 1/4 [Notebook]

seq2seq

• Lecture 28: ο Variational AE 2
• Lecture 28: ο Variational AE 2 [Notebook]
• Lecture 28: ο Variational AE 2 [Notebook]
• Lecture 27: τ AE the Bayesian Approach
• Lecture 27: τ AE the Bayesian Approach [Notebook]
• Lecture 27: τ AE the Bayesian Approach [Notebook]
• Lecture 26: ⍵ AutoEncoder (AE)
• Lecture 26: ⍵ AutoEncoder (AE) [Notebook]
• Lecture 26: ⍵ AutoEncoder (AE) [Notebook]
• Lecture 26: ⍵ AutoEncoder (AE) [Notebook]
• Lecture 25: 🤖 (Transformers II) NLP 4/4
• Lecture 25: 🤖 (Transformers II) NLP 4/4 [Notebook]
• Lecture 24: 🧠 Attention (Transformers I) NLP 3/4
• Lecture 24: 🧠 Attention (Transformers I) NLP 3/4 [Notebook]

Sequences

• Lecture 19: ς RNNs

Setup

• Lecture 3: Setup and Review of statsmodels
• Lecture 3: Setup and Review of statsmodels [Notebook]
• Lecture 3: Setup and Review of statsmodels [Notebook]

Smoothers

• Lecture 5_5: Smoothers pyGAM csaps

SOTA

• Lecture 18: 𝗈 State of the art models (SOTA) and Transfer Learning

Stats Models

• Lecture 4: Splines Smoothers and GAMs (part 3)
• Lecture 2: Splines Smoothers and GAMs (part 2)

Statsmodels

• Lecture 8: Clustering in Python (Lab)
• Lecture 3: Setup and Review of statsmodels
• Lecture 3: Setup and Review of statsmodels [Notebook]
• Lecture 3: Setup and Review of statsmodels [Notebook]

Syntax

• Lecture 22: 💬 Language Modelling NLP 1/4
• Lecture 22: 💬 Language Modelling NLP 1/4 [Notebook]

Taylor’s theorem

• Lecture 4: Splines Smoothers and GAMs (part 3)
• Lecture 2: Splines Smoothers and GAMs (part 2)

Teaching Fellows

• Lecture 1: Splines Smoothers and GAMs (part 1)

TSTR

• Lecture 30: ⍺ GANs DOS
• Lecture 30: ⍺ GANs DOS [Notebook]

Unsupervised Learning

• Lecture 8: Clustering in Python (Lab)
• Lecture 6: Unsupervised learning cluster analysis (part 2)
• Lecture 5: Unsupervised learning cluster analysis (part 1)

vanishing & exploding gradient

• Advanced Section 2: Echo-State Reservoir Computing

Variational Autoencoders

• Advanced Section 4: Variational Inference

VGG

• Lecture 18: 𝗈 State of the art models (SOTA) and Transfer Learning

Windowing

• Lecture 19: ς RNNs

Word Embeddings

• Advanced Section 3: Word Embeddings

Word2Vec

• Advanced Section 3: Word Embeddings

WordNet

• Lecture 23: 🔢 Language Representations NLP 2/4
• Lecture 23: 🔢 Language Representations NLP 2/4 [Notebook]